Reliability‐based decision fusion scheme for cooperative spectrum sensing
Bibliographic record
Abstract
In this study, the authors propose a reliability‐based cooperative decision fusion scheme which considers the reliability of the secondary users (SUs') local decisions when making a final decision at the fusion centre in cognitive radios. The authors use past information about the local and global decisions to estimate the reliability of the sensing decision obtained from each SU and then reflect this difference in reliability in the weighting of each SU's decision. The authors formulate the problem of minimising the probability of sensing error at the fusion centre, subject to a limit on the network probability of detection, as a constrained non‐linear integer programming problem. To solve this problem, the authors implement an iterative solution based on the generalised non‐linear Lagrangian relaxation. Simulation results show that our proposed solution can achieve optimal results with zero duality gap using only a few number of iterations. Results also demonstrate that the proposed reliability‐based fusion scheme provides performance improvement, in terms of the minimum probability of sensing error, when compared to the OR and AND fusion schemes. This improvement is more pronounced as the number of users increases since by assigning weights differently to users, the multiuser diversity gain is better exploited.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".